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References

1 
R. V. Klyuev, I. D. Morgoev, A. D. Morgoeva, O. A. Gavrina, N. V. Martyushev, E. A. Efremenkov, and Q. Mengxu, “Methods of forecasting electric energy consumption: A literature review,” Energies, vol. 15, no. 23, pp. 8919, 2022.DOI
2 
M. Jacob, C. Neves, and D. V. Greetham, “Forecasting and assessing risk of individual electricity peaks,” Springer Nature, 2020.URL
3 
A. Gupta, M. Chawla, and N. Tiwari, “Electricity Power Consumption Forecasting Techniques: A survey,” Proceedings of the International Conference on Innovative Computing & Communication (ICICC), 2022.DOI
4 
G. Mahalakshmi, S. Sridevi, and S. Rajaram, “A survey on forecasting of time series data,” 2016 IEEE International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE), pp. 1-8, 2016.DOI
5 
K. Benidis, S. S. Rangapuram, V. Flunkert, Y. Wang, D. Maddix, and C. Turkmen, “Deep Learning for Time Series Forecasting,” ACM Computing Suveys, vol. 55(6), no. 121, pp. 1-36, 2022.DOI
6 
Z. Shen, Y. Zang, J. Lu, J. Xu, and G. Xiao, “A novel time series forecasting model with deep learning,” Neurocomputing, vol. 396, pp. 302-313, 2020.DOI
7 
B. Lim, and S. Zohern, “Time-series forecasting with deep learning: a survey,” Philosophical Transactions of the Royal Society A, vol. 379, no. 2194, pp. 20200209, 2021.DOI
8 
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol 30, 2017.URL
9 
T. Zhang, Y. Zhang, W. Cao, J. Bian, X. Yi, S. Zheng, and J. Li, “Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures,” arXiv preprint arXiv:2207.01186, 2022.DOI
10 
F. Chollet, “Xception: Deep Learing with Depthwise Separable Convolutions,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251-1258, 2017.URL
11 
A. Tokgöz, and G. Ünal, “A RNN based time series approach for forecasting turkish electricity load,” 2018 IEEE 26th Signal processing and communication applications conference (SIU), pp. 1-4, 2018.DOI
12 
J. S. Caicedo-Vivas, and W. Alfonso-Morales, “Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator,” Energies, vol. 16, no. 23, pp. 7878, 2023.DOI
13 
M. Jurado, M. Samper, and R. Rosés, “An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting,” Electric Power Systems Research, vol. 217, pp. 109153, 2023.DOI
14 
D. Wang, J. Gan, J. Mao, F. Chen, and L. Yu, “Forecasting power demand in China with a CNN-LSTM model including multimodal information,” Energy, vol. 263, pp. 126012, 2023.DOI
15 
J. H. Im, Y. R. Seong, and H. R. Oh, “A Method of Multi-model Machine Learning for Electrical Energy Prediction Accuracy Improvement,” The transactions of The Korean Institute of Electrical Engineers, vol. 71, no. 6, pp. 876-883, 2022.DOI
16 
J. W. Chan, and C. K. Yeo, “A Transformer based approach to electricity load forecasting,” The Electricity Journal, vol. 37, no. 2, pp. 107370, 2024.DOI
17 
D. K. Kim, and K. S. Kim, “A Convolutional Transformer Model for Multivariate Time Series Prediction,” IEEE Access, vol. 10, pp. 101319-101329, 2022.DOI
18 
A. González-Vidal, F. Jiménez, A. F. Gómez-Skarmeta, “A methodology for energy multivariate time series forecasting in smart buildings based on feature selection,” Energy and Buildings, vol. 196, pp. 71-82, 2019.DOI
19 
H. H. Htun, M. Biehl, and N. Petkov, “Survey of feature selection and extraction techniques for stock market prediction,” Financial Innovation, vol. 9, no. 1, pp. 26, 2023.DOI
20 
J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, “Feature Selection: A Data Perspective,” ACM computing surveys (CSUR), vol. 50(6), no. 94, pp. 1-45, 2017.DOI